How an Agent Built a 3D Paris Gallery by Chaining Two Hugging Face Spaces A coding agent built a 3D Paris gallery by chaining two Hugging Face Spaces β€” one for image generation and one for 3D reconstruction β€” without any human intervention in asset creation. The agent used the output of the first Space as input for the second, then assembled the results into a cinematic Three.js viewer. The demonstration shows how AI agents can now assemble multimedia software by gluing together documented, callable model blocks rather than building from scratch. πŸ‘ 192 TripoSplat Generate 3D Gaussian models from a single image I asked a coding agent to build a beautiful website showcasing the monuments of Paris as 3D Gaussian splats. I never opened an image generator. I never touched a 3D reconstruction tool. The agent produced every asset the images and the 3D splats by calling two Hugging Face Spaces directly, then wired them into a cinematic viewer. Here's the result, live as a static Space: This post is about how that's possible now, and why I think it's a preview of how a lot of multimedia software gets built from here on. Mitchell Hashimoto recently described a shift he calls the building block economy https://mitchellh.com/writing/building-block-economy : the most effective path to software is no longer a polished monolith, but small, well-documented components that others increasingly agents can assemble. His key observation: AI is okay at building everything from scratch, but it is really good at gluing together proven pieces. That thesis has mostly been told with code libraries. But the same forces are hitting multimedia AI . The hard part of using a state-of-the-art image model, a video model, a TTS model, or a 3D reconstruction model was never the model. It was the integration: SDKs, weights, GPUs, input formats, polling. If each model were instead a documented, callable block, an agent could glue them together the same way it globs together npm packages. That's exactly what Hugging Face Spaces have quietly become. agents.md The Hub hosts thousands of state-of-the-art models a huge share of them open-weights , and most are deployed as interactive Spaces . As of now, every Gradio Space also exposes a plain-text agents.md https://huggingface.co/docs/hub/en/spaces-agents that tells an agent curl https://huggingface.co/spaces/VAST-AI/TripoSplat/agents.md returns everything needed in one shot: the schema URL, the call and poll templates, how to upload files, and the auth hint: API schema: GET .../gradio api/info Call endpoint: POST .../gradio api/call/v2/{endpoint} {"param name": value, ...} Poll result: GET .../gradio api/call/{endpoint}/{event id} File inputs: POST .../gradio api/upload -F "files=@file.ext" Auth: Bearer $HF TOKEN No client library. No hardcoded integration. An agent reads that, and it can drive the Space end to end. Set an HF TOKEN https://huggingface.co/settings/tokens and you're going. You can find these instructions on any Gradio Space via its The real unlock is chaining : the output of one Space becomes the input to the next. Prompt β†’ image β†’ 3D. That's the whole pipeline behind this gallery. The agent chained two Spaces: VAST-AI/TripoSplat .ply from each single image. Image in, 3D out.Generated image Reconstructed splat The six source images the agent generated, all isolated on black, ready for single-image 3D reconstruction: From there the agent did the "glue" work too. It noticed TripoSplat outputs are Y-down and flipped them upright, auto-framed each monument, compressed the .ply files to .ksplat ~3Γ— smaller, so they load fast , built a Three.js viewer with a scroll-to-switch and drag-to-rotate UI, and deployed the whole thing as a static Space. The only human inputs were taste-level: "make it zoomed out," "replace the obelisk with something better for splatting," "the transition lingers too long." Several of those steps were the agent reacting to reality . A wide glass pyramid splats poorly. A thin obelisk is dull. A single-view reconstruction infers the back. That is exactly the "outsourced R&D, fast iteration" loop the building-block economy predicts, except the R&D was a conversation. The real test of a building block is how cheaply you can reuse it. Once this pipeline existed, spinning up entirely new galleries cost about one sentence each. "Create a similar Space with splats for Japan," then the same for Egypt, and the agent did the rest: six monument images, six splats, compression, a viewer, and a deployed Space, per country.